Parcel shipping is a highly competitive industry, particularly for large commercial customers of parcel shipping companies. These shipping clients typically purchase shipping services through a competitive bidding process. For instance, a shipping client takes bids on an annual basis to select a parcel shipping company to handle substantially all of the client's shipping needs for an annual period. Although air and ground shipping services are sometimes bid separately, within these categories each contract is typically bid as an “all requirements” shipping contract covering a portfolio of shipping services for an extended period of time, such as, for example, a year.
A major parcel shipping company may have thousands of commercial contracts of this nature that are competitively re-bid on an annual basis. In addition, each shipping client may have aspects to their shipping needs that, if properly reflected in the bid price, can improve the parcel shipping company's likelihood of winning the bid. For example, some customers may be more expensive to provide service to than others, due to factors such as average size of the parcels, the typical number of parcels in each pickup, the distance and particular locations shipped to, the proximity of pickup sites to transportation routes, and many other factors. In practice, knowledge of these special factors allows the parcel shipping company to profitably offer many potential clients a discount or incentive to win their business.
Traditionally, bid pricing in the parcel shipping industry has been assisted by computer systems that estimate the cost of serving individual customers, taking into account the special factors listed above. However, many traditional cost-of-service based bidding systems have a number of drawbacks as pricing tools for competitively bid goods and services. Specifically, these systems lack the ability to factor the market response of customers and competitors into the pricing decisions. This is because, in large-part, these systems are cost-focused, even though customers may demand products and services that are tailored to their specific needs. Thus, target pricing systems have been developed to reflect market and competitor response characteristics into bid pricing to attempt to address the drawbacks of traditional cost-of-service based bidding systems.
In many instances, these systems include market response models that take market and competitor response characteristics into account. In addition, these models may be configured to utilize logistic regression to calculate coefficients for both “brand preference” and “price sensitivity” input variables in order to determine the best estimate for the probability of winning the bid. However, many times, the models used in legacy systems are not able to calculate a sufficiently accurate coefficient for price sensitivity, and as a result, this parameter is fixed at a constant value. Therefore, a need exists for providing an expanded data set that allows the logistic regression approach to mathematically calculate the coefficient for price sensitivity with greater accuracy.